Extracting Card Data with Card Models

1. A computer-implemented method for extracting card information, comprising: receiving, by one or more computing devices, an image of a card from a camera; identifying, by the one or more computing devices, a first area of the image, the first area being selected as a potential location of one or more digits on the image; performing, by the one or more computing devices, a classification algorithm on data encompassed by the first area; identifying, by the one or more computing devices, one or more lines of potential digits on the image based on the results of the application of the classification; comparing, by the one or more computing devices, the image to one or more card models, the one or more models comprising digit distribution patterns of data displayed on the image; performing, by the one or more computing devices, an optical character recognition algorithm on areas of the card that are anticipated by the one or more computing devices as comprising digits based on the application of the card models and the identified lines; determining, by the one or more computing devices, a confidence level of one or more results of the applications of the optical character recognition algorithm to the image; and verifying, by the one or more computing devices, a particular result based at least in part on a determination that the confidence level is the highest of the determined confidence levels.

2. The method of claim 1 , further comprising selecting, by the one or more computing devices, the one or more card models based at least in part on stored user data indicating card types that are associated with the user.

3. The method of claim 1 , further comprising comparing, by the one or more computing devices, the model associated with the authenticated result to a database of card types to determine a card type of the card in the image.

4. The method of claim 1 , wherein the identifying of one or more lines of potential digits is performed based on Random Sample Consensus.

5. The method of claim 1 , wherein the card models represent digit distribution patterns for known card issuers.

6. The method of claim 1 , wherein the lines are identified by analyzing, by the one or more computing devices, a position of the identified digits relative to each other and fitting lines to the positions.

7. The method of claim 1 , wherein the card is a credit card, a debit card, an identification card, a loyalty card, an access card, or a stored value card.

8. A computer program product, comprising: a non-transitory computer-readable storage device having computer-executable program instructions embodied thereon that when executed by a computer cause the computer to extract card information, comprising: computer-executable program instructions to receive an image of a card from a camera; computer-executable program instructions to identify a first area of the image, the first area being selected as a potential location of one or more digits on the image; computer-executable program instructions to perform a classification algorithm on data encompassed by the first area; computer-executable program instructions to identify one or more lines of potential digits on the image based on the results of the application of the classification; computer-executable program instructions to compare the image to one or more card models, the one or more models comprising digit distribution patterns of data displayed on the image; computer-executable program instructions to perform an optical character recognition algorithm on areas of the card that are anticipated by the one or more computing devices as comprising digits based on the application of the card models and the identified lines; computer-executable program instructions to determine a confidence level of one or more results of the applications of the optical character recognition algorithm to the image; and computer-executable program instructions to verify a particular result based at least in part on a determination that the confidence level is the highest of the determined confidence levels.

9. The computer program product of claim 8 , the computer-executable program instructions further comprising selecting the one or more card models based at least in part on stored user data indicating card types that are associated with the user.

10. The computer program product of claim 8 , the computer-executable program instructions further comprising comparing the model associated with the authenticated result to a database of card types to determine a card type of the card in the image.

11. The computer program product of claim 8 , wherein the classification algorithm is a support vector machine.

12. The computer program product of claim 8 , wherein the card models represent digit distribution patterns for known card issuers.

13. The computer program product of claim 8 , wherein the lines are identified by analyzing a position of the identified digits relative to each other and fitting lines to the positions.

14. The computer program product of claim 8 , wherein the card is a credit card, a debit card, an identification card, a loyalty card, an access card, or a stored value card.

15. A system for extracting financial card information with relaxed alignment, the system comprising: a storage device; a processor communicatively coupled to the storage device, wherein the processor executes application code instructions that are stored in the storage device to cause the system to: receive an image of a card from a camera; identify a first area of the image, the first area being selected as a potential location of one or more digits on the image; perform a classification algorithm on data encompassed by the first area; identify one or more lines of potential digits on the image based on the results of the application of the classification; compare the image to one or more card models, the one or more models comprising digit distribution patterns of data displayed on the image; perform an optical character recognition algorithm on areas of the card that are anticipated by the one or more computing devices as comprising digits based on the application of the card models and the identified lines; determine a confidence level of one or more results of the applications of the optical character recognition algorithm to the image; and verify a particular result based at least in part on a determination that the confidence level is the highest of the determined confidence levels.

16. The system of claim 15 , the processor executing further application code instructions that are stored in the storage device and that cause the system to select the one or more card models based at least in part on stored user data indicating card types that are associated with the user.

17. The system of claim 15 , the processor executing further application code instructions that are stored in the storage device and that cause the system to compare the model associated with the authenticated result to a database of card types to determine a card type of the card in the image.

18. The system of claim 15 , wherein the classification algorithm is a support vector machine.

19. The system of claim 15 , wherein the card models represent digit distribution patterns for known card issuers.

20. The system of claim 15 , wherein the card is a credit card, a debit card, an identification card, a loyalty card, an access card, or a stored value card.